ThoughtViz

Tirupattur, Praveen; Rawat, Yogesh Singh; Spampinato, Concetto; Shah, Mubarak

doi:10.1145/3240508.3240641

Cited by 61 publications

(17 citation statements)

References 19 publications

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“…For example, in the task of decoding image classes with EEG recordings, when subjects were required to watch images of different classes, a decoding accuracy of 82.90% was reported for the 40-way classification by Spampinato et al [3]. With their EEG dataset, subsequent studies reported a higher decoding accuracy (98.30%, [4]), high performance on image retrieval, and even image generation from EEG [5], [6], [7].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Decoding Different Reach-and-Grasp Movements Using Noninvasive Electroencephalogram

Zhang

Wang

et al. 2021

Front. Neurosci.

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Grasping is one of the most indispensable functions of humans. Decoding reach-and-grasp actions from electroencephalograms (EEGs) is of great significance for the realization of intuitive and natural neuroprosthesis control, and the recovery or reconstruction of hand functions of patients with motor disorders. In this paper, we investigated decoding five different reach-and-grasp movements closely related to daily life using movement-related cortical potentials (MRCPs). In the experiment, nine healthy subjects were asked to naturally execute five different reach-and-grasp movements on the designed experimental platform, namely palmar, pinch, push, twist, and plug grasp. A total of 480 trials per subject (80 trials per condition) were recorded. The MRCPs amplitude from low-frequency (0.3–3 Hz) EEG signals were used as decoding features for further offline analysis. Average binary classification accuracy for grasping vs. the no-movement condition peaked at 75.06 ± 6.8%. Peak average accuracy for grasping vs. grasping conditions of 64.95 ± 7.4% could be reached. Grand average peak accuracy of multiclassification for five grasping conditions reached 36.7 ± 6.8% at 1.45 s after the movement onset. The analysis of MRCPs indicated that all the grasping conditions are more pronounced than the no-movement condition, and there are also significant differences between the grasping conditions. These findings clearly proved the feasibility of decoding multiple reach-and-grasp actions from noninvasive EEG signals. This work is significant for the natural and intuitive BCI application, particularly for neuroprosthesis control or developing an active human–machine interaction system, such as rehabilitation robot.

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Section: Introduction and Related Workmentioning

confidence: 99%

Decoding Different Reach-and-Grasp Movements Using Noninvasive Electroencephalogram

Zhang

Wang

et al. 2021

Front. Neurosci.

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“…They assessed an enormous number of convolutional neural networks on an EEG decoding task, and exhibited that progresses from the domain of deep learning, including exponential linear units and batch normalization, are essential for accomplishing high exactnesses accuracies. Tirupattur et al [27] introduced an EEG-based deep learning approach, namely ThoughtViz, for visualizing human thoughts using a GAN-based framework. Kavasidis et al [11] suggested a structure for producing the visual stimuli content data through EEG data.…”

Section: Related Workmentioning

confidence: 99%

Brain-media: A Dual Conditioned and Lateralization Supported GAN (DCLS-GAN) towards Visualization of Image-evoked Brain Activities

Fares

Zhong

Jiang

2020

Proceedings of the 28th ACM International Conference on Multimedia

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out and the results show that our proposed deep framework significantly outperforms the representative existing state-of-the-arts under several settings, especially in terms of both visualization and classification of brain responses to the evoked images. For the convenience of research dissemination, we make the source code openly accessible for downloading at GitHub. 1 CCS CONCEPTS • Computing methodologies → Unsupervised learning; Neural networks.

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“…For instance, Palazzo et al [41] utilized recurrent neural networks to extract class-dependent representations of EEG signals, and applied the learned EEG manifold to condition image generation by employing GANs. Likewise, Tirupattur et al [42] took the encoded EEG signal as input and generated the corresponding images through GANs. Inspired by the above works, we argue that it is equally feasible to transform images into EEG signals through GANs in a cross-modality manner, expecting to deep learn human mind.…”

Section: Introductionmentioning

confidence: 99%

Image2Brain: a cross-modality model for blind stereoscopic image quality ranking

Shen,

Li,

Pan

et al. 2023

J. Neural Eng.

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Objective. Human beings perceive stereoscopic image quality through the cerebral visual cortex, which is a complex brain activity. As a solution, the quality of stereoscopic images can be evaluated more accurately by attempting to replicate the human perception from electroencephalogram (EEG) signals on image quality in a machine, which is different from previous stereoscopic image quality assessment methods focused only on the extraction of image features. Approach. Our proposed method is based on a novel image-to-brain (I2B) cross-modality model including a spatial-temporal EEG encoder (STEE) and an I2B deep convolutional generative adversarial network (I2B-DCGAN). Specifically, the EEG representations are first learned by STEE as real samples of I2B-DCGAN, which is designed to extract both quality and semantic features from the stereoscopic images by a semantic-guided image encoder, and utilize a generator to conditionally create the corresponding EEG features for images. Finally, the generated EEG features are classified to predict the image perceptual quality level. Main results. Extensive experimental results on the collected brain-visual multimodal stereoscopic image quality ranking database, demonstrate that the proposed I2B cross-modality model can better emulate the visual perception mechanism of the human brain and outperform the other methods by achieving an average accuracy of 95.95 % . Significance. The proposed method can convert the learned stereoscopic image features into brain representations without EEG signals during testing. Further experiments verify that the proposed method has good generalization ability on new datasets and the potential for practical applications.

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ThoughtViz

Cited by 61 publications

References 19 publications

Decoding Different Reach-and-Grasp Movements Using Noninvasive Electroencephalogram

Decoding Different Reach-and-Grasp Movements Using Noninvasive Electroencephalogram

Brain-media: A Dual Conditioned and Lateralization Supported GAN (DCLS-GAN) towards Visualization of Image-evoked Brain Activities

Image2Brain: a cross-modality model for blind stereoscopic image quality ranking

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